Jan Kloppenborg Moller
Jan Kloppenborg Moller
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Cited by
Non‐parametric probabilistic forecasts of wind power: required properties and evaluation
P Pinson, HA Nielsen, JK Møller, H Madsen, GN Kariniotakis
Wind Energy: An International Journal for Progress and Applications in Wind …, 2007
Time-adaptive quantile regression
JK Møller, HA Nielsen, H Madsen
Computational Statistics & Data Analysis 52 (3), 1292-1303, 2008
Short-term probabilistic forecasting of wind speed using stochastic differential equations
EB Iversen, JM Morales, JK Møller, H Madsen
International Journal of Forecasting 32 (3), 981-990, 2016
Probabilistic forecasts of solar irradiance using stochastic differential equations
EB Iversen, JM Morales, JK Møller, H Madsen
Environmetrics 25 (3), 152-164, 2014
Inhomogeneous Markov models for describing driving patterns
EB Iversen, JK Møller, JM Morales, H Madsen
IEEE Transactions on Smart Grid 8 (2), 581-588, 2016
From state dependent diffusion to constant diffusion in stochastic differential equations by the Lamperti transform
JK Møller, H Madsen
DTU Informatics, 2010
Integration of electric vehicles in low voltage danish distribution grids
JR Pillai, P Thøgersen, J Møller, B Bak-Jensen
2012 IEEE power and energy society general meeting, 1-8, 2012
Hidden Markov Models for indirect classification of occupant behaviour
J Liisberg, JK Møller, H Bloem, J Cipriano, G Mor, H Madsen
Sustainable Cities and Society 27, 83-98, 2016
Probabilistic forecasts of wind power generation by stochastic differential equation models
JK Møller, M Zugno, H Madsen
Journal of Forecasting 35 (3), 189-205, 2016
Grey‐box modelling of flow in sewer systems with state‐dependent diffusion
A Breinholt, FÖ Thordarson, JK Møller, M Grum, PS Mikkelsen, H Madsen
Environmetrics 22 (8), 946-961, 2011
A Markov-Switching model for building occupant activity estimation
S Wolf, JK Møller, MA Bitsch, J Krogstie, H Madsen
Energy and Buildings 183, 672-683, 2019
Leveraging stochastic differential equations for probabilistic forecasting of wind power using a dynamic power curve
EB Iversen, JM Morales, JK Møller, PJ Trombe, H Madsen
Wind Energy 20 (1), 33-44, 2017
A formal statistical approach to representing uncertainty in rainfall–runoff modelling with focus on residual analysis and probabilistic output evaluation–Distinguishing …
A Breinholt, JK Møller, H Madsen, PS Mikkelsen
Journal of hydrology 472, 36-52, 2012
Towards model predictive control: online predictions of ammonium and nitrate removal by using a stochastic ASM
PA Stentoft, T Munk-Nielsen, L Vezzaro, H Madsen, PS Mikkelsen, ...
Water Science and Technology 79 (1), 51-62, 2019
Evaluation of probabilistic flow predictions in sewer systems using grey box models and a skill score criterion
FÖ Thordarson, A Breinholt, JK Møller, PS Mikkelsen, M Grum, H Madsen
Stochastic Environmental Research and Risk Assessment 26, 1151-1162, 2012
Ctsm-r user guide
R Juhl, NR Kristensen, P Bacher, J Kloppenborg, H Madsen
Technical University of Denmark 2, 2013
Parameter estimation in a simple stochastic differential equation for phytoplankton modelling
JK Møller, H Madsen, J Carstensen
Ecological modelling 222 (11), 1793-1799, 2011
ctsmr-continuous time stochastic modeling in R
R Juhl, JK Møller, H Madsen
arXiv preprint arXiv:1606.00242, 2016
Cross-validation of a glucose-insulin-glucagon pharmacodynamics model for simulation using data from patients with type 1 diabetes
SL Wendt, A Ranjan, JK Møller, S Schmidt, CB Knudsen, JJ Holst, ...
Journal of diabetes science and technology 11 (6), 1101-1111, 2017
Modeling and prediction using stochastic differential equations
R Juhl, JK Møller, JB Jørgensen, H Madsen
Prediction methods for blood glucose concentration: design, use and …, 2016
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